Partial occlusion detection of object boundary

Partial occlusion is a difficult problem in computer vision since whether the object is changed or occluded is ambiguous, especially when distinguishing it only from the object boundary. In this paper, we proposed a novel idea to solve this problem by taking shape matching as a morphing processing. A mass-spring model is constructed from the point set which is sampled from a template (or reference) object boundary by moving it to a target object which is deformed and/or occluded. From the morphing processing, sufficient information can be obtained and an accurate detection of occlusion is performed. By using of the proposed method, the application scope of occlusion detection is expanded while other method cannot be performed which need color, texture, or motion information. The experiments performed on synthetic and real world images proved the satisfactory performance of the proposed method.